sa_fri1: Synthetic regression and classification datasets for...

In rminer: Data Mining Classification and Regression Methods

Description

5 Synthetic regression (sa_fri1, sa_ssin, sa_psin, sa_int2, sa_tree) and 4 classification (sa_ssin_2, sa_ssin_n2p, sa_int2_3c, sa_int2_8p) datasets for measuring input importance of supervised learning models

Usage

data(sa_fri1)

Format

A data frame with 1000 observations on the following variables.

xn

input (numeric or factor, depends on the dataset)

y

output target (numeric or factor, depends on the dataset)

Details

Check reference or source for full details

Source

See references

References

• To cite the Importance function, sensitivity analysis methods or synthetic datasets, please use:
  P. Cortez and M.J. Embrechts.
  Using Sensitivity Analysis and Visualization Techniques to Open Black Box Data Mining Models.
  In Information Sciences, Elsevier, 225:1-17, March 2013.
  http://dx.doi.org/10.1016/j.ins.2012.10.039
  

Examples

data(sa_ssin)
print(summary(sa_ssin))
## Not run: plot(sa_ssin$x1,sa_ssin$y)

Example output

       x1                 x2                x3                x4          
 Min.   :  0.1312   Min.   :  3.289   Min.   :  2.995   Min.   :  0.1204  
 1st Qu.:248.3085   1st Qu.:241.062   1st Qu.:261.454   1st Qu.:279.3098  
 Median :507.6361   Median :487.281   Median :506.569   Median :509.8421  
 Mean   :503.0023   Mean   :491.512   Mean   :510.092   Mean   :508.0603  
 3rd Qu.:756.9140   3rd Qu.:735.221   3rd Qu.:766.872   3rd Qu.:745.0632  
 Max.   :999.3183   Max.   :999.637   Max.   :999.660   Max.   :999.5433  
       y          
 Min.   :0.04501  
 1st Qu.:0.44795  
 Median :0.61135  
 Mean   :0.58357  
 3rd Qu.:0.72217  
 Max.   :0.90978  

rminer documentation built on Aug. 28, 2020, 5:08 p.m.